modify Tensor shape

mindspore/ccsrc/pybind_api/ir/tensor_py.cc

@@ -406,6 +406,11 @@ REGISTER_PYBIND_DEFINE(Tensor, ([](const py::module *m) {
                                   return std::make_shared<Tensor>(data_type, GetShapeFromTuple(shape));
                                 }),
                                 py::arg("dtype"), py::arg("shape"))
+                           .def(py::init([](const TypePtr &type_ptr, const py::list &shape) {
+                                  auto data_type = type_ptr ? type_ptr->type_id() : TypeId::kNumberTypeFloat64;
+                                  return std::make_shared<Tensor>(data_type, GetShapeFromTuple(shape));
+                                }),
+                                py::arg("dtype"), py::arg("shape"))
                            .def(py::init([](const py::array &input, const TypePtr &type_ptr) {
                                   return TensorPy::MakeTensor(input, type_ptr);
                                 }),

mindspore/common/tensor.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """Tensor implementation."""
+import numbers
 import numpy as np
 
 from mindspore import log as logger
@@ -43,6 +44,9 @@ class Tensor(Tensor_):
         shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of
             output. Default: None.
         init (class:'Initializer'): the information of init data.
+            'init' is used for delayed initialization in parallel mode. Usually, it is not recommended to
+            use 'init' interface to initialize parameters in other conditions. If 'init' interface is used
+            to initialize parameters, the `init_data` API need to be called to convert `Tensor` to the actual data.
 
     Outputs:
         Tensor, with the same shape as `input_data`.
@@ -76,6 +80,9 @@ class Tensor(Tensor_):
         if ((input_data is not None and init is None) or (input_data is None and init is not None)) is False:
             raise TypeError("input_data and init can not be None at the same time.")
 
+        if isinstance(shape, numbers.Number):
+            shape = (shape,)
+
         # If input_data is tuple/list/numpy.ndarray, it's support in check_type method.
         if init is None:
             validator.check_value_type('input_data', input_data, (Tensor_, np.ndarray, list, tuple, float, int, bool),
@@ -575,6 +582,7 @@ class Tensor(Tensor_):
     def init_data(self, slice_index=None, shape=None, opt_shard_group=None):
         """
         Get the tensor format data of this Tensor.
+        The init_data function can be called once for the same tensor.
 
         Args:
             slice_index (int): Slice index of a parameter's slices.